伤寒论PBL教学法提高中医八年制学生中医临床思维构建的应用

中医临床思维是中医专业学生临床能力的关键。《伤寒论》是培养中医学生建立中医临床思维的核心课程。中医八年制较传统的中医博士研究生培养模式而言,培养时间大大缩短、教学要求高。为进一步提高中医八年制学员的临床思维能力,我们在《伤寒论》的教学过程中,分别在理论授课、临床实践中采用了PBL教学方法,教学效果良好。现介绍自己教学方法以及教学体会,以供同行参考交流。     

Improved prediction of brain age using multimodal neuroimaging data

Brain age prediction based on imaging data and machine learning (ML) methods has great potential to provide insights into the development of cognition and mental disorders. Though different ML models have been proposed, a systematic comparison of ML models in combination with imaging features derived from different modalities is still needed. In this study, we evaluate the prediction performance of 36 combinations of imaging features and ML models including deep learning. We utilize single and multimodal brain imaging data including MRI, DTI, and rs‐fMRI from a large data set with 839 subjects. Our study is a follow‐up to the initial work (Liang et al., 2019. Human Brain Mapping) to investigate different analytic strategies to combine data from MRI, DTI, and rs‐fMRI with the goal to improve brain age prediction accuracy. Additionally, the traditional approach to predicting the brain age gap has been shown to have a systematic bias. The potential nonlinear relationship between the brain age gap and chronological age has not been thoroughly tested. Here we propose a new method to correct the systematic bias of brain age gap by taking gender, chronological age, and their interactions into consideration. As the true brain age is unknown and may deviate from chronological age, we further examine whether various levels of behavioral performance across subjects predict their brain age estimated from neuroimaging data. This is an important step to quantify the practical implication of brain age prediction. Our findings are helpful to advance the practice of optimizing different analytic methodologies in brain age prediction.     

Altered resting‐state functional connectivity and effective connectivity of the habenula in irritable bowel syndrome: A cross‐sectional and machine learning study

Irritable bowel syndrome (IBS) is a disorder involving dysfunctional brain–gut interactions characterized by chronic recurrent abdominal pain, altered bowel habits, and negative emotion. Previous studies have linked the habenula to the pathophysiology of negative emotion and pain. However, no studies to date have investigated habenular function in IBS patients. In this study, we investigated the resting‐state functional connectivity (rsFC) and effective connectivity of the habenula in 34 subjects with IBS and 34 healthy controls and assessed the feasibility of differentiating IBS patients from healthy controls using a machine learning method. Our results showed significantly enhanced rsFC of the habenula‐left dorsolateral prefrontal cortex (dlPFC) and habenula‐periaqueductal grey (PAG, dorsomedial part), as well as decreased rsFC of the habenula‐right thalamus (dorsolateral part), in the IBS patients compared with the healthy controls. Habenula‐thalamus rsFC was positively correlated with pain intensity (r = .467, p = .005). Dynamic causal modeling (DCM) revealed significantly decreased effective connectivity from the right habenula to the right thalamus in the IBS patients compared to the healthy controls that was negatively correlated with disease duration (r = ?.407, p = .017). In addition, IBS was classified with an accuracy of 71.5% based on the rsFC of the habenula‐dlPFC, habenula‐thalamus, and habenula‐PAG in a support vector machine (SVM), which was further validated in an independent cohort of subjects (N = 44, accuracy = 65.2%, p = .026). Taken together, these findings establish altered habenular rsFC and effective connectivity in IBS, which extends our mechanistic understanding of the habenula's role in IBS.     

K-Means Clustering to Elucidate Vulnerable Subpopulations Among Medicare Patients Undergoing Total Joint Arthroplasty

BackgroundThe role of preoperative laboratory values for risk stratification following joint arthroplasty is currently ambiguous. In order to improve upon existing risk stratification within joint arthroplasty, this study sought to define novel phenotypes of total hip or total knee arthroplasty patients based entirely on preoperative laboratory measures. These phenotypes (“clusters”) were compared to elucidate statistically and clinically significant differences in outcomes.MethodsA total of 134,252 patients were gathered from the National Surgical Quality Improvement Program database between 2005 and 2015. “K-means” with 3 clusters was applied using 9 preoperative laboratory values: sodium, blood urea nitrogen (BUN), creatinine, albumin, bilirubin, white blood cell count, hematocrit, platelet count, and international normalized ratio of prothrombin values (INR). Outcome measures included 30-day readmissions, severe adverse events, and discharge to nonhome.ResultsCluster 2 was characterized by elevated preoperative BUN, creatinine, and INR and demonstrated almost twice the rate of adverse events (3.52% vs 2.20% and 2.22%), 30-day readmissions (6.39% vs 3.31% and 3.71%), and discharge to nonhome (47.97% vs 30.50% and 35.85%). Cluster 3 was characterized by a slightly higher risk of discharge to nonhome than cluster 1 and was overwhelmingly female (79.5% female, 35.8% discharge to nonhome). Cluster 1 represents the lowest-risk subgroup, experiencing the lowest rates of readmissions, adverse events, and discharge to nonhome.ConclusionPreoperative laboratory values, namely BUN, creatinine, and INR, are useful in identifying patients at risk of adverse outcomes. This analysis supports the existing surgical literature pushing for preoperative hydration as a targeted intervention to expedite recovery.     

Engaging Students with Visual Stories: Using Infographics in Nursing Education

Infographics represent visual stories which engage and guide audiences to the realization of patterns, relationships, and conclusions. Infographics help capture attention and make complex knowledge understandable, making them a useful teaching-learning strategy. Although infographics can be effective to engage students, they are infrequently used because of educator inexperience with this technology. This article provides an overview for incorporating infographics in nursing education including evidence-informed step-by-step guidelines and resources for designing and sharing infographics.     

Radiomics for classification of bone mineral loss: A machine learning study

PurposeThe purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machine learning approaches.Materials and methodsA total of 147 patients were included in this retrospective single-center study. There were 12 men and 135 women with a mean age of 56.88 ± 10.6 (SD) years (range: 28–87 years). For each patient, seven regions including four lumbar and three femoral including trochanteric, intertrochanteric and neck were segmented on bone mineral densitometry images and 54 texture features were extracted from the regions. The performance of four feature selection methods, including classifier attribute evaluation (CLAE), one rule attribute evaluation (ORAE), gain ratio attribute evaluation (GRAE) and principal components analysis (PRCA) along with four classification methods, including random forest (RF), random committee (RC), K-nearest neighbor (KN) and logit-boost (LB) were evaluated. Four classification categories, including osteopenia vs. normal, osteoporosis vs. normal, osteopenia vs. osteoporosis and osteoporosis + osteopenia vs. osteoporosis were examined for the defined seven regions. The classification model performances were evaluated using the area under the receiver operator characteristic curve (AUC).ResultsThe AUC values ranged from 0.50 to 0.78. The combination of methods RF + CLAE, RF + ORAE and RC + ORAE yielded highest performance (AUC = 0.78) in discriminating between osteoporosis and normal state in the trochanteric region. The combinations of RF + PRCA and LB + PRCA had the highest performance (AUC = 0.76) in discriminating between osteoporosis and normal state in the neck region.ConclusionThe machine learning radiomic approach can be considered as a new method for bone mineral deficiency disease classification using bone mineral densitometry image features.     

Neurophysiology of motor skill learning in chronic stroke

ObjectiveMotor learning is relevant in chronic stroke for acquiring compensatory strategies to motor control deficits. However, the neurophysiological mechanisms underlying motor skill acquisition with the paretic upper limb have received little systematic investigation. The aim of this study was to assess the modulation of corticomotor excitability and intracortical inhibition within ipsilesional primary motor cortex (M1) during motor skill learning.MethodsTen people at the chronic stage after stroke and twelve healthy controls trained on a sequential visuomotor isometric wrist extension task. Skill was quantified before, immediately after, 24 hours and 7 days post-training. Transcranial magnetic stimulation was used to examine corticomotor excitability and short- and long-interval intracortical inhibition (SICI and LICI) pre- and post-training.ResultsThe patient group exhibited successful skill acquisition and retention, although absolute skill level was lower compared with controls. In contrast to controls, patients’ ipsilesional corticomotor excitability was not modulated during skill acquisition, which may be attributed to excessive ipsilesional LICI relative to controls. SICI decreased after training for both patient and control groups.ConclusionsOur findings indicate distinct inhibitory networks within M1 that may be relevant for motor learning after stroke.SignificanceThese findings have potential clinical relevance for neurorehabilitation adjuvants aimed at augmenting the recovery of motor function.     

Differences between medical school and PGY1 learning outcomes: An explanation for new graduates not being “work ready”?

Abstract

Background: Widespread concerns about new medical graduates’ ‘work readiness’ may reflect, in part, differences in mandatory learning outcomes for medical students and new medical graduates.

Purpose: To examine differences between required medical student and PGY1 (first year resident) training program outcomes, and the nature and magnitude of these differences.

Method: Comparison, systematic identification and thematic analysis of differences between the graduate outcomes in the Australian Medical Council Standards for the Assessment and Accreditation of Primary Medical Programs and those in the New Zealand Curriculum Framework for Prevocational Training.

Results: The relationship between these outcome statements were categorized as: essentially similar; continuity; partial discontinuity; and complete discontinuity of learning trajectory. Areas requiring substantial new learning may reflect medical schools’ focus on individual student performance, and on learning and assessments based on single episodes of often uncomplicated illness. This contrasted with a post-graduate focus on integrated health care delivery by teams and management of complex illnesses over the whole patient care journey.

Conclusions: Characterizing these marked differences between pre-graduate and postgraduate standards, within a trajectory of learning, explains some of the difficulties in students’ preparation for work readiness. These could inform learning interventions to support new graduates’ professional development to ensure patient safety. Development and revision of accreditation standards should include formal review against the expectations of the preceding and succeeding phases of learning.